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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Question answering system using Q & A site corpus Query expansion and answer candidate evaluation.

Kanako Komiya1, Yuji Abe, Hajime Morita

  • 1Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho-Kotanei, Tokyo, 184-8588 Japan.

Springerplus
|November 21, 2013
PubMed
Summary

This study enhances Question Answering (QA) systems using a Q&A corpus. Novel methods for query expansion and answer evaluation significantly improve system performance and accuracy.

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Area of Science:

  • Natural Language Processing
  • Information Retrieval

Background:

  • Question Answering (QA) systems aim to provide accurate answers to natural language queries.
  • Existing QA systems often struggle with relevant document retrieval and candidate answer evaluation.

Purpose of the Study:

  • To propose and evaluate two novel methods for improving QA system performance.
  • To enhance the relevant document retrieval and candidate answer evaluation modules.

Main Methods:

  • Modified mutual information for query expansion, calculating word relatedness within Q&A pairs.
  • Combined evaluation of candidate answers using Web relevance scores and translation probability.

Main Results:

  • The proposed query expansion method significantly improved accuracy and Mean Reciprocal Rank (MRR).
  • The proposed candidate answer evaluation method demonstrated significant improvements in MRR.

Conclusions:

  • The developed methods offer effective enhancements for QA systems.
  • Utilizing Q&A site corpora with refined retrieval and evaluation techniques boosts QA performance.